• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.13-24
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• 2020

There are about nine million power line poles and 1.3 million kilometers of the power line for electric power distribution in Korea. Maintenance of such a large number of electric power facilities requires a lot of manpower and time. Recently, various fault diagnosis techniques using artificial intelligence have been studied. Therefore, in this paper, proposes a power line deflection detect system using artificial intelligence and computer vision technology in images taken by vision system. The proposed system proceeds as follows. (i) Detection of transmission tower using object detection system (ii) Histogram equalization technique to solve the degradation in image quality problem of video data (iii) In general, since the distance between two transmission towers is long, a panoramic video stitching process is performed to grasp the entire power line (iv) Detecting deflection using computer vision technology after applying power line detection algorithm This paper explain and experiment about each process.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of the Korea Society for Simulation
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• v.26 no.4
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• pp.35-41
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• 2017

With the advances in Internet over Things, the demand in diverse electronic devices such as mobile phones and sensors has been rapidly increasing and boosting up the researches on those products. Semiconductor materials, devices, and fabrication processes are becoming more diverse and complicated, which accompanies finding parameters for an optimal fabrication process. In order to find the parameters, a process simulation before fabrication or a real-time process control system during fabrication can be used, but they lack incorporating the feedback from post-fabrication data and compatibility with older equipment. In this research, we have developed an artificial intelligence based simulator, which finds parameters for an optimal process and controls process equipment. In order to apply the control concept to all the equipment in a fabrication sequence, we have developed a prototype for a manipulator which can be installed over an existing buttons and knobs in the equipment and controls the equipment communicating with the AI over the Internet. The AI is based on the deep learning to find process parameters that will produce a device having target electrical characteristics. The proposed simulator can control existing equipment via the Internet to fabricate devices with desired performance and, therefore, it will help engineers to develop new devices efficiently and effectively.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.53-59
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• 2021

In this paper, we propose a BERGPT-chatbot, a domestic AI chatbot that can alleviate negative emotions based on text input such as 'Replika'. We made BERGPT-chatbot into a chatbot capable of mitigating negative emotions by pipelined two models, KR-BERT and KoGPT2-chatbot. We applied a creative method of giving emotions to unrefined everyday datasets through KR-BERT, and learning additional datasets through KoGPT2-chatbot. The development background of BERGPT-chatbot is as follows. Currently, the number of people with depression is increasing all over the world. This phenomenon is emerging as a more serious problem due to COVID-19, which causes people to increase long-term indoor living or limit interpersonal relationships. Overseas artificial intelligence chatbots aimed at relieving negative emotions or taking care of mental health care, have increased in use due to the pandemic. In Korea, Psychological diagnosis chatbots similar to those of overseas cases are being operated. However, as the domestic chatbot is a system that outputs a button-based answer rather than a text input-based answer, when compared to overseas chatbots, domestic chatbots remain at a low level of diagnosing human psychology. Therefore, we proposed a chatbot that helps mitigating negative emotions through BERGPT-chatbot. Finally, we compared BERGPT-chatbot and KoGPT2-chatbot through 'Perplexity', an internal evaluation metric for evaluating language models, and showed the superity of BERGPT-chatbot.

• Phonetics and Speech Sciences
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• v.15 no.4
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• pp.71-80
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• 2023

This study primarily aimed to develop an automated stuttering identification and classification method using artificial intelligence technology. In particular, this study aimed to develop a deep learning-based identification model utilizing the convolutional neural networks (CNNs) algorithm for Korean speakers who stutter. To this aim, speech data were collected from 9 adults who stutter and 9 normally-fluent speakers. The data were automatically segmented at the phrasal level using Google Cloud speech-to-text (STT), and labels such as 'fluent', 'blockage', prolongation', and 'repetition' were assigned to them. Mel frequency cepstral coefficients (MFCCs) and the CNN-based classifier were also used for detecting and classifying each type of the stuttered disfluency. However, in the case of prolongation, five results were found and, therefore, excluded from the classifier model. Results showed that the accuracy of the CNN classifier was 0.96, and the F1-score for classification performance was as follows: 'fluent' 1.00, 'blockage' 0.67, and 'repetition' 0.74. Although the effectiveness of the automatic classification identifier was validated using CNNs to detect the stuttered disfluencies, the performance was found to be inadequate especially for the blockage and prolongation types. Consequently, the establishment of a big speech database for collecting data based on the types of stuttered disfluencies was identified as a necessary foundation for improving classification performance.

• The Journal of Bigdata
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• v.4 no.2
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• pp.115-126
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• 2019

There have been many discussions on export indices in trade exports, but there is no definite trade export index which can be explained by objective indicators. Korea International Trade Association (KITA), Korea Trade-Investment Promotion Agency (KOTRA), etc., but we are currently in the process of thinking about ways to express the capabilities of exporting companies. In this study, we constructed the AI data sets by setting the activity indicators such as the size of the company and the credit score, the number of transaction customers, the number of transactions, the number of items, the transaction volume, and the transaction period as features, Lightgbm. Using the Graph Neural Network as an industrial cluster classification model, the export live index which expresses the exportable capacity among companies, items, and business groups was calculated. This includes the past activity of the company from the current calculating index Objectivity.

• Journal of Internet Computing and Services
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• v.21 no.2
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• pp.9-18
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• 2020

In the field of image processing, a method of detecting and counting passengers as moving objects when getting on and off the bus has been studied. Among these technologies, one of the artificial intelligence techniques, the deep learning technique is used. As another method, a method of detecting an object using a stereo vision camera is also used. However, these techniques require expensive hardware equipment because of the computational complexity of used to detect objects. However, most video equipments have a significant decrease in computational processing power, and thus, in order to detect passengers on the bus, there is a need for an image processing technology suitable for various equipment using a relatively low computational technique. Therefore, in this paper, we propose a technique that can efficiently obtain the number of passengers on the bus by detecting the contour of the object through the background subtraction suitable for low-cost equipment. Experiments have shown that passengers were counted with approximately 70% accuracy on lower-end machines than those equipped with stereo vision camera.

• Journal of the Korea Convergence Society
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• v.8 no.4
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• pp.1-8
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• 2017

In the modern society, the size of the fashion market is continuously increasing both overseas and domestic. When purchasing a product through e-commerce, the evaluation data for the product created by other consumers has an effect on the consumer's decision to purchase the product. By analysing the consumer's evaluation data on the product the company can reflect consumer's opinion which can leads to positive affect of performance to company. In this paper, we propose a method to construct a model to analyze user's sentiment using word embedding space formed by learning review data of amazon fashion products. Experiments were conducted by learning three SVM classifiers according to the number of positive and negative review data using the formed word embedding space which is formed by learning 5.7 million Amazon review data.. Experimental results showed the highest accuracy of 88.0% when learning SVM classifier using 50,000 positive review data and 50,000 negative review data.

• Proceedings of the Korean Society of Disaster Information Conference
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• 2017.11a
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• pp.260-261
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• 2017

최근 사회구조의 복잡화, 산업구조의 다변화, 기후변화 등에 의해 자연재해 및 산업재해, 도시재난이 급증하고, 그 규모 또한 대형화하고 있다. 이로 인해 에너지, 통신, 교통, 금융 등 공공 인프라의 피해가 급증하면서 작은 재해도 큰 재난으로 변하는 예가 늘어나고 있다. 한편 현대사회에 대한 IT의 관여도가 급속도로 늘어나면서 IT 서비스의 궁극적인 형태이자, 모든 산업을 수용하는 개념의 플랫폼(Platform)이 IT를 넘어서 글로벌 사회의 절대적 지배자로 등장했다. 또한 전 세계 유저들의 관점에서 보면 개개인들이 손에 든 스마트폰이 생활의 모든 분야에 걸쳐 소통, 정보, 쇼핑, 제보, 오락 등 모든 활동의 수단으로 절대적 가치를 창출하고 있다. 이는 스마트폰이 가진 스마트 데이터 생산 및 공유 기능에서 비롯된다. 이처럼 스마트 데이터를 기반으로 한 IT플랫폼이 중요한 위치를 점하지만, 아직 재난관리 분야에서 이를 본격적으로 도입, 활용하지 못하고 있다는 점은 큰 문제이다. 국내의 사정을 보면 다행히 벤처기업들을 중심으로 이 같은 플랫폼 구축 움직임이 시작되었으며, 여기에 활용될 데이터 자원을 창출할 수 있는 솔루션 및 특허기술들 역시 속속 등장하고 있다. 시민들이 재난현장을 스마트폰으로 실시간 공유하면 이 스마트 데이터들이 이미지 및 음향정보, 위치기반(GPS)정보, 시각정보, 3D정보, 빅데이터 정보, 센서정보 등으로 분류되어 플랫폼 안에서 인공지능(AI) 딥러닝 방식에 의해 분석되고, 이를 즉시 재난당국 및 시민들에게 재난긴급문자 등 자동으로 경보로 전해주는 것이 이 플랫폼의 핵심 기능이다. 몇몇 벤처기업이 보유한 특허기술을 기반으로 공공자본이 투입되어 이러한 플랫폼이 구축될 경우 국내 재난관리 수준의 획기적 발전은 물론 전 세계를 시장으로 한 플랫폼 수출 또는 글로벌 재난정보 수집능력에서도 엄청난 힘을 발휘할 것으로 기대된다.

• Annual Conference of KIPS
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• 2018.05a
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• pp.377-380
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• 2018

인간의 추론 능력이란 문제에 주어진 조건을 보고 문제 해결에 필요한 것이 무엇인지를 논리적으로 생각해 보는 것으로 문제 상황 속에서 일정한 규칙이나 성질을 발견하고 이를 수학적인 방법으로 법칙을 찾아내거나 해결하는 능력을 말한다. 이러한 인간인지 능력과 유사한 인공지능 시스템을 개발하는데 있어서 핵심적 도전은 비구조적 데이터(unstructured data)로부터 그 개체들(object)과 그들간의 관계(relation)에 대해 추론하는 능력을 부여하는 것이라고 할 수 있다. 지금까지 딥러닝(deep learning) 방법은 구조화 되지 않은 데이터로부터 문제를 해결하는 엄청난 진보를 가져왔지만, 명시적으로 개체간의 관계를 고려하지 않고 이를 수행해왔다. 최근 발표된 구조화되지 않은 데이터로부터 복잡한 관계 추론을 수행하는 심층신경망(deep neural networks)은 관계추론(relational reasoning)의 시도를 이해하는데 기대할 만한 접근법을 보여주고 있다. 그 첫 번째는 관계추론을 위한 간단한 신경망 모듈(A simple neural network module for relational reasoning) 인 RN(Relation Networks)이고, 두 번째는 시각적 관찰을 기반으로 실제대상의 미래 상태를 예측하는 범용 목적의 VIN(Visual Interaction Networks)이다. 관계 추론을 수행하는 이들 심층신경망(deep neural networks)은 세상을 객체(objects)와 그들의 관계(their relations)라는 체계로 분해하고, 신경망(neural networks)이 피상적으로는 매우 달라 보이지만 근본적으로는 공통관계를 갖는 장면들에 대하여 객체와 관계라는 새로운 결합(combinations)을 일반화할 수 있는 강력한 추론 능력(powerful ability to reason)을 보유할 수 있다는 것을 보여주고 있다. 본 논문에서는 관계 추론을 수행하는 심층신경망(deep neural networks) 중에서 Sort-of-CLEVR 데이터 셋(dataset)을 사용하여 RN(Relation Networks)의 성능을 재현 및 관찰해 보았으며, 더 나아가 파라미터(parameters) 튜닝을 통하여 RN(Relation Networks) 모델의 성능 개선방법을 제시하여 보았다.